In the case of StyleGAN, it has been shown that the codes provided to different network layers can influence different semantic attributes. Thus, by considering editing directions in the \(\mathcal {W}+\)  space [1]} – the latent space where each layer of StyleGAN can be provided with a different code \(w_{i} \in \mathcal {W}\)  – we can identify which layers are most strongly tied to a given change. While we are interested in changes that cannot be achieved through mere latent code interpolations, we can still consider such editing directions locally (i.e. at each iteration along the optimization process) and ask: `given the current version of the model, which layers are most relevant to our change?'.
